The Alignment Handbook is a collection of recipes for aligning language models with human and AI preferences using techniques like reinforcement learning from human feedback.
The handbook addresses the challenge of making language models behave in ways that match human values and expectations. It provides practical, tested approaches for implementing alignment techniques, with a focus on robustness and reproducibility. The recipes cover methods for training models to follow human preferences and incorporate feedback from both human evaluators and AI systems, enabling developers to move beyond base model behavior toward more controlled and desirable outputs.
Teams building production language models or working on safety and behavior control should consider this resource. It suits projects where alignment with specific values or preferences is a core requirement, particularly those using the Hugging Face ecosystem. The handbook is especially valuable for organizations that want established, battle-tested recipes rather than building alignment pipelines from scratch, as it provides concrete implementations that have been validated across different model sizes and training scenarios.
The project maintains active engagement with the community through regular updates to its recipes and documentation. Development activity shows consistent refinement of existing approaches based on practical experience. The handbook incorporates feedback from real-world deployment scenarios, ensuring that the recipes reflect what works in practice rather than theory alone.
